Pikes_Data <- read.csv("data/Pikes_Data_for_R .csv")
AFT_data <- read.csv("data/AFT_Data.csv")
#Modify the sample names, for ease of reading, where PP still stands for Pikes Peak, and they are numbered 1 - 6 with the lowest elevation sample being 1 and highest elevation sample being 6
Pikes_Data <- Pikes_Data %>%
mutate (papername =
ifelse(Sample_Name == "PP2084", "PP1", #If True, add label PP2
ifelse( Sample_Name == "PP2479", "PP2", #If True, add label PP2
ifelse (Sample_Name == "PP2907", "PP3", #If true, add label PP3
ifelse (Sample_Name == "PP3597", "PP4", #If true, add label PP4
ifelse (Sample_Name == "PP3971", "PP5", "PP6") #if true, add label PP5, ELSE add the label PP6
)
)
)
)
)
This section makes plots for:
1. Pikes Peak Elevation - date
2. Pikes Peak Date-eU
3. Pikes Peak Elevation vs. date with each point colored by eU
4. Pikes Peak date vs. grain size
3. Pikes Peak AFT data (Kelley and Chapin, 2004)
## [1] "Excludes bottom two AFT samples at 1777m and 1866m"
## Warning: Removed 2 rows containing missing values (geom_point).
Grains excluded from models:
* Sample: PP1, grain:Z32, rownumber: 6 - this grain has an eU of 151.5 and a date of 312.9, which is ~ 300 Ma younger than grains w/ comprable eU
grains.not.modeled <- c(6)
Pikes_Data <- Pikes_Data %>%
mutate(
Rownumber= row_number(),
Donotuse = (Rownumber %in% grains.not.modeled),
bindata= cut(eU, c(0,150,350,500,900,2500)) #these are my bin cutoffs
)
## # A tibble: 5 x 15
## bindata N RawDate_mean Rawdate_15perce… Rawdate_SD CorrDate_mean
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 (0,150] 7 546. 82.0 30.0 670.
## 2 (150,3… 6 519. 77.8 67.1 627.
## 3 (350,5… 6 295. 44.2 88.0 389.
## 4 (500,9… 7 208. 31.2 86.8 264.
## 5 (900,2… 5 120 18 36.7 141.
## # … with 9 more variables: CorrDate_15percent <dbl>, CorrDate_SD <dbl>,
## # mean_rs <dbl>, U <dbl>, Th <dbl>, Sm <dbl>, eU <dbl>, He <dbl>, FT <dbl>